"""simple docstring"""


from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
	from PIL import Image

	from ..image_utils import load_image

if is_torch_available():
	import torch

	from transformers.modeling_outputs import BaseModelOutput

	from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

a				=logging.get_logger(__name__)






@add_end_docstrings(__lowerCAmelCase      )
class   __UpperCAmelCase		(      __lowerCAmelCase      ):

				def __init__( self      ,					**_lowerCamelCase ):
										super().__init__(**_lowerCamelCase )

										if self.framework == "tf":
																raise ValueError(F'''The {self.__class__} is only available in PyTorch.''' )

										requires_backends(self      ,					"vision" )
										self.check_model_type(_lowerCamelCase )

				def __call__( self      ,					_lowerCamelCase      ,					_lowerCamelCase = None      ,					**_lowerCamelCase      ,					):
										if "text_queries" in kwargs:
																lowerCamelCase__   =kwargs.pop("text_queries" )

										if isinstance(_lowerCamelCase      ,					(str, Image.Image) ):
																lowerCamelCase__   ={"image": image, "candidate_labels": candidate_labels}
										else:
																lowerCamelCase__   =image
										lowerCamelCase__   =super().__call__(_lowerCamelCase      ,					**_lowerCamelCase )
										return results

				def 						_a      ( self      ,					**_lowerCamelCase ):
										lowerCamelCase__   ={}
										if "threshold" in kwargs:
																lowerCamelCase__   =kwargs["threshold"]
										if "top_k" in kwargs:
																lowerCamelCase__   =kwargs["top_k"]
										return {}, {}, postprocess_params

				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   =load_image(inputs["image"] )
										lowerCamelCase__   =inputs["candidate_labels"]
										if isinstance(_lowerCamelCase      ,					_lowerCamelCase ):
																lowerCamelCase__   =candidate_labels.split("," )

										lowerCamelCase__   =torch.tensor([[image.height, image.width]]      ,					dtype=torch.intaa )
										for i, candidate_label in enumerate(_lowerCamelCase ):
																lowerCamelCase__   =self.tokenizer(_lowerCamelCase      ,					return_tensors=self.framework )
																lowerCamelCase__   =self.image_processor(_lowerCamelCase      ,					return_tensors=self.framework )
																yield {
																    "is_last": i == len(_lowerCamelCase ) - 1,
																    "target_size": target_size,
																    "candidate_label": candidate_label,
																    **text_inputs,
																    **image_features,
																}

				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   =model_inputs.pop("target_size" )
										lowerCamelCase__   =model_inputs.pop("candidate_label" )
										lowerCamelCase__   =model_inputs.pop("is_last" )

										lowerCamelCase__   =self.model(**_lowerCamelCase )

										lowerCamelCase__   ={"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
										return model_outputs

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=None ):
										lowerCamelCase__   =[]
										for model_output in model_outputs:
																lowerCamelCase__   =model_output["candidate_label"]
																lowerCamelCase__   =BaseModelOutput(_lowerCamelCase )
																lowerCamelCase__   =self.image_processor.post_process_object_detection(
																    outputs=_lowerCamelCase      ,					threshold=_lowerCamelCase      ,					target_sizes=model_output["target_size"] )[0]

																for index in outputs["scores"].nonzero():
																						lowerCamelCase__   =outputs["scores"][index].item()
																						lowerCamelCase__   =self._get_bounding_box(outputs["boxes"][index][0] )

																						lowerCamelCase__   ={"score": score, "label": label, "box": box}
																						results.append(_lowerCamelCase )

										lowerCamelCase__   =sorted(_lowerCamelCase      ,					key=lambda _lowerCamelCase : x["score"]      ,					reverse=_lowerCamelCase )
										if top_k:
																lowerCamelCase__   =results[:top_k]

										return results






				def 						_a      ( self      ,					_lowerCamelCase ):
										if self.framework != "pt":
																raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch." )
										lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =box.int().tolist()
										lowerCamelCase__   ={
										    "xmin": xmin,
										    "ymin": ymin,
										    "xmax": xmax,
										    "ymax": ymax,
										}
										return bbox



"""simple docstring"""


from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   List[Any]:




						'''simple docstring'''



						def is_in_circle(__lowerCAmelCase  ,       __lowerCAmelCase							) -> bool:
												lowerCamelCase__   =sqrt((x**2) + (y**2)							)
												# Our circle has a radius of 1, so a distance
												# greater than 1 would land outside the circle.
												return distance_from_centre <= 1

						# The proportion of guesses that landed in the circle
						lowerCamelCase__   =mean(
						    int(is_in_circle(uniform(-1.0  ,       1.0							)  ,       uniform(-1.0  ,       1.0							)							)							)
						    for _ in range(__lowerCAmelCase							)							)
						# The ratio of the area for circle to square is pi/4.
						lowerCamelCase__   =proportion * 4
						print(F'''The estimated value of pi is {pi_estimate}'''							)
						print(F'''The numpy value of pi is {pi}'''							)
						print(F'''The total error is {abs(pi - pi_estimate							)}'''							)





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase = 0.0  ,       __lowerCAmelCase = 1.0  ,       )				->   float:




						'''simple docstring'''



						return mean(
						    function_to_integrate(uniform(__lowerCAmelCase  ,       __lowerCAmelCase							)							) for _ in range(__lowerCAmelCase							)							) * (max_value - min_value)





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase = 0.0  ,       __lowerCAmelCase = 1.0							)				->   None:




						'''simple docstring'''



						def identity_function(__lowerCAmelCase							) -> float:
												return x

						lowerCamelCase__   =area_under_curve_estimator(
						    __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)
						lowerCamelCase__   =(max_value * max_value - min_value * min_value) / 2

						print("******************"							)
						print(F'''Estimating area under y=x where x varies from {min_value} to {max_value}'''							)
						print(F'''Estimated value is {estimated_value}'''							)
						print(F'''Expected value is {expected_value}'''							)
						print(F'''Total error is {abs(estimated_value - expected_value							)}'''							)
						print("******************"							)





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   None:




						'''simple docstring'''



						def function_to_integrate(__lowerCAmelCase							) -> float:
												return sqrt(4.0 - x * x							)

						lowerCamelCase__   =area_under_curve_estimator(
						    __lowerCAmelCase  ,       __lowerCAmelCase  ,       0.0  ,       2.0							)

						print("******************"							)
						print("Estimating pi using area_under_curve_estimator"							)
						print(F'''Estimated value is {estimated_value}'''							)
						print(F'''Expected value is {pi}'''							)
						print(F'''Total error is {abs(estimated_value - pi							)}'''							)
						print("******************"							)


if __name__ == "__main__":
	import doctest

	doctest.testmod()


